Worldly · 19 hours ago
Senior Data Engineer - Data Platform and Analytics
United States
Full-time
Remote
Senior Level
$135K/yr - $165K/yr
5+ years expWorldly is the world’s most comprehensive impact intelligence platform, delivering real data to businesses on impacts within their supply chain. They are seeking a hands-on data engineer with a passion for sustainability to build, operate, maintain, and evolve systems that support internal analytics and customer-facing analytics platforms.
SustainabilityFashionConsultingBig DataConsumer GoodsSoftwareInformation TechnologyLogisticsApparelData ManagementReal TimeService IndustrySupply Chain Management
Responsibilities
You will collaborate with stakeholders across the organization to design and implement scalable, cloud-based data solutions, integrating generative AI to drive innovation
You will work closely with cross-functional stakeholders (finance, product, marketing, customer support, tech, data science) to enable trusted data products for internal decision making and external-facing tools
You will have a leading role in the development of a data lake resource to complement our existing data warehouse, enabling greater flexibility in analytics and reporting
You will work with AWS services, automation tools, machine learning, and generative AI to enhance efficiency, stability, security, and performance
Operate and evolve our Postgres data warehouse: schema design, performance tuning, indexing, access controls, and so on
Build analytics-ready datasets supporting sustainability measurement, supply-chain insights, and business metrics
Deploy and maintain multiple instances of Cube.dev semantic layers with standardized configuration, CI/CD workflows, and governance practices (including documentation of processes, configurations, and troubleshooting)
Establish clear and consistent metric definitions and versioning across dashboards and analytics surfaces
Support integration and deployment of genAI-enabled workflows, especially NLP-based use cases (classification, extraction, normalization, embeddings/similarity)
Ensure that our data infrastructure is “AI-ready”
In collaboration with data scientists, research and develop practical transition plans for evolving selected relational/warehouse data structures into a graph-based knowledgebase (for use with the Neo4j framework), including candidate use cases, data modeling approach, migration sequencing, and operational considerations (performance, governance, lineage, and security)
Maintain and stabilize existing DBT pipelines that underpin reporting and analytics, including automation for incremental processing/scheduling, data quality monitoring, and performance tuning
Lead operational support and modernization planning: rapid triage and root-cause resolution for pipeline issues, and evaluation/prototyping of next-generation transformation approaches with clear, low-risk transition plans in partnership with analytics and engineering stakeholders
Build ingestion and ETL processes using S3, Glue, Lambda, and AppFlow
Integrate data from third-party systems and APIs (e.g., Zendesk, HubSpot, NetSuite, other platform data) with strong auditability and operational resilience
Qualification
Required
5+ years of professional experience in data engineering, analytics engineering, or data platform engineering
Advanced SQL expertise and strong experience with relational databases, especially Postgres
Strong Python development skills applied to data pipelines, automation, and operational tooling
Strong Git-based development practices (branching, PRs, code review)
Demonstrated experience developing and supporting DBT transformations and operational workflows
Hands-on experience building AWS ingestion/ETL workflows using services such as S3, IAM, Glue, Lambda, CloudFormation (or other IaC), and AppFlow
Practical DevOps experience: CI/CD pipelines, Git/GitHub workflows, and containerization fundamentals (Docker)
Experience with analytics data modeling and metric definition practices
Experience implementing automated monitoring/alerting and data quality controls for pipelines and critical datasets
Experience operating production data systems (including data quality tests, regression checks, validation frameworks, incident triage, root-cause analysis, runbooks, reliability improvements)
Experience working closely with analytics teams and cross-functional stakeholders; familiarity with Jira/Confluence and Agile delivery
Familiarity with data security practices (PII protection, encryption controls, access management)
Preferred
Experience with semantic layers (Cube.dev preferred)
Familiarity with genAI/NLP enablement
Exposure to graph databases (knowledge graphs) and related concepts (Neo4j preferred)
Benefits
Medical, Dental, and Vision Insurance are offered through multiple PPO options. Worldly covers 90% employee premium and 60% spouse/dependent premium.
Company-sponsored 401k with up to 4% match for US employees.
Incentive Stock Options.
100% Parental Paid Leave.
Unlimited PTO.
12 paid company holidays.
Earn a competitive salary and performance-based bonuses.
Get healthcare, retirement matching, and equity for US employees.
Use the office stipend to get the supplies you need—combat Zoom fatigue with no-meeting Fridays.
Flexible time off. Take the time you need to recharge. Our culture encourages team members to explore and rest to be their best selves.
We're remote, not lonely. Join the culture committee, coffee chats, or a variety of other interest groups.
Company
Worldly
Worldly is the leading sustainability data and analytics platform for the consumer goods industry, empowering brands, retailers, and manufacturers to turn primary data into strategic action.
51-200 employees
Funding
Current Stage
Growth StageTotal Funding
$64MKey Investors
Galvanize Climate Solutions,Silversmith Capital PartnersBuckhill Capital LP
2022-04-27Series B· $50M
2019-05-01Series A· $14M
Leadership Team
Francois Thrower
Chief Financial Officer
Kevin Vranes
Chief Product Officer
Recent News
Company data provided by crunchbase